Computerized clinical databases have great potential as tools for assessing the efficacy of new diagnostic and therapeutic modalities, for monitoring the quality of health care delivery, and for support of medical research. Because of this potential, many clinical databases have recently been developed. However, because the stored records are typically those of patients not on rigid protocols and not randomized to therapy, a number of problems complicate the study of hypotheses: strong sources of bias in compared subsets, missing and censored data, complex causual mechanisms and time relationships, and lack of uniformity in stages and definitions of disease. The objective of our research is to facilitate the use of chronic disease databases as a convenient and reliable source for the discovery and testing of medical hypotheses. The fundamental premise of our research is that derivation of knowledge from data is guided considerably by pre-existing analytic and domain knowledge. Hence, we have developed a system - the RX Project - which combines with the database, a machine readable knowledge base (KB) of medicine and statistics. The KB is a frame-based, taxonomic hierarchy of objects which represent diseases and therapies. A collection of time-dependent predicates allows patient records to be abstracted. The KB also contains knowledge of statistical techniques encoded into production rules. These allow semi-automated study design. The current proposal details numerous extensions to the RX System in the context of a variety of studies. The ARAMIS Time-Oriented Database will be used to address issues of therapeutics in the rheumatic diseases. The methodology is generalizable to all chronic diseases.